Global Sensitivity Analysis Based on Polynomial Chaos

Abstract

Abstract: Global sensitivity analysis method based on polynomial chaos and variance decomposition is reviewed comprehensively. In order to alleviate "curse of dimensionality" coming from high-dimensional random spaces or high-order polynomial chaos expansions, several approaches such as least square regression, sparse grid quadrature and sparse recovery based on l₁ minimization (i. e. compressive sensing) are used to reduce sample size of collocation points that needed by non-intrusive polynomial chaos method. With computation of Sobol global sensitivity indices for several benchmark response models including Ishigami function, Sobol function, Corner peak function and Morris function, effective implementations of polynomial chaos method for variance-based global sensitivity analysis are exhibited.

Key words: polynomial chaos, global sensitivity, curse of dimensionality, sparse recovery

CLC Number:

O213

HU Jun, ZHANG Shudao. Global Sensitivity Analysis Based on Polynomial Chaos[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2016, 33(1): 1-14.

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